Computer-based interlocutor understanding using classifying conversation segments

ABSTRACT

Computer-based natural language understanding of input and output for a computer interlocutor is improved using a method of classifying conversation segments from transcribed conversations. The improvement includes one or more methods of splitting transcribed conversations into groups related to a conversation ontology using metadata; identifying dominant paths of conversational behavior by counting the frequency of occurrences of the behavior for a given path; creating a conversation model comprising conversation behaviors, metadata, and dominant paths; and using the conversation model to assign a probability score for a matched input to the computer interlocutor or a generated output from the computer interlocutor.

PRIORITY BENEFIT CLAIM

This is a continuation patent application of U.S. patent applicationSer. No. 17/164,510, filed on Feb. 1, 2021, which was a continuation ofU.S. patent application Ser. No. 16/201,188 filed on Nov. 27, 2018 (nowU.S. Pat. No. 10,929,611), which claimed benefit of U.S. ProvisionalPatent Application 62/594,610 filed on Dec. 5, 2017, all by Jonathan E.Eisenzopf.

FIELD OF THE INVENTION

This is a continuation patent application of U.S. patent applicationSer. No. 17/164,510, filed on Feb. 1, 2021, which was a continuation ofU.S. patent application Ser. No. 16/201,188 filed on Nov. 27, 2018 (nowU.S. Pat. No. 10,929,611), which claimed benefit of U.S. ProvisionalPatent Application 62/594,610 filed on Dec. 5, 2017, all by Jonathan E.Eisenzopf. The present invention relates to certain improvements ofcomputer functionality to understand conversational inputs to anInteractive Voice Response, chat, messaging, or virtual assistantdevice.

BACKGROUND OF INVENTION

Interactive Voice Response (IVR) systems are commonly used by a widevariety of companies, government agencies, and private organizations toallow users of a telephone to navigate a hierarchy of menus to obtaininformation, conduct transactions, and connect to human agents forfurther help. These systems, however, do not offer a natural,conversational interface, but rather require the user to conform to themenu structure provided by the IVR system, which leads to frustration,errors, delays, and loss of customer affinity for the service, company,agency or organization.

Computer-based “chat” systems, messaging applications, and virtualassistants such as Amazon™ Alexa™ and Google Assistant™ allow forunstructured natural language input, but this input format isfundamentally incompatible with the input format of existing IVRsystems.

SUMMARY OF THE EXEMPLARY Embodiments of the Invention

Computer-based natural language understanding of input and output for acomputer interlocutor is improved using a method of classifyingconversation segments from transcribed conversations. The improvementincludes one or more methods of splitting transcribed conversations intogroups related to a conversation ontology using metadata; identifyingdominant paths of conversational behavior by counting the frequency ofoccurrences of the behavior for a given path; creating a conversationmodel comprising conversation behaviors, metadata, and dominant paths;and using the conversation model to assign a probability score for amatched input to the computer interlocutor or a generated output fromthe computer interlocutor.

BRIEF DESCRIPTION OF THE DRAWINGS

The figures presented herein, when considered in light of thisdescription, form a complete disclosure of one or more embodiments ofthe invention, wherein like reference numbers in the figures representsimilar or same elements or steps.

FIG. 1 depicts an improved data processing system and its relatedcomponents according to at least one embodiment of the presentinvention.

FIG. 2 depicts one or more methods according to the present inventionperformed by the improved data processing system to classify a pluralityof conversation transcriptions between two or more interlocutors.

FIG. 3 illustrates an exemplary conversation classification methodincluding splitting a plurality of transcribed conversations betweenmultiple interlocutors into a plurality of conversation segments.

FIG. 4 shows an exemplary embodiment of a method for dominant weightingfor a dominant path modeler.

FIG. 5 illustrates an exemplary topic classification method used by atopic classifier to identify the correct topic of conversation.

FIG. 6 depicts an exemplary weighted conversation model using a weightedconversation model.

FIG. 7 sets forth an exemplary conversation ontology used to forrule-based decision making to split transcribed conversations intosegments for classification by the improved data processing system.

FIG. 8 illustrates an exemplary arrangement of computers, devices, andnetworks according to at least one embodiment of the present invention.

DETAILED DESCRIPTION OF ONE OR MORE EXEMPLARY EMBODIMENT(S) OF THEINVENTION

The present inventor has realized that there is an unmet need in the artof computing and user interface to enable an IVR to interface to a userthrough a conversational interface, especially through a digital virtualassistant. Certain improvements are disclosed herein that improve theease of use of an IVR-provided service through particular user interfaceenhancements, while simultaneously improving the utilization of computerusage of computing resources such as memory footprint, processingbandwidth, and communications bandwidth to yield higher levels ofsimultaneously-served users by a single computing platform, therebyreducing the cost of the service to the operator.

This invention relates to a data processing system that processes audio,text and/or visual input for a computer interlocutor by creating andusing a computer-based and computer-maintained conversation modelcomprising a plurality of topics comprising a plurality of probableinputs and outputs of a conversation based on a plurality of recordedconversations between a plurality of interlocutors.

The computer interlocutor resides on a computer with attached storageand memory that contains one or more processing units. The computerinterlocutor creates responses displayed via an output mechanism such asan attached computer monitor or embedded visual screen or audio speakerattached to or embedded in the computer or computing device based onmatching user inputs from an input device such as a connected keyboardor microphone attached to a computer or computing device.

Computer-based natural language understanding of input and output for acomputer interlocutor is improved using a method, disclosed herein, ofclassifying conversation segments, which includes one or more of thefollowing computer-performed actions, steps or processes:

-   -   a. receiving conversation data from transcribed conversations,        such as between two people, an online chat or a text messaging        system, a speech recognition system, or a chatbot or voicebot        system;    -   b. splitting transcribed conversations into groups related to a        conversation ontology using metadata; identifying dominant paths        of conversational behavior by counting the frequency of        occurrences of the behavior for a given path;    -   c. creating a conversation model comprising conversation        behaviors, metadata, and dominant paths;    -   d. using the conversation model to assign a probability score        for a matched input to the computer interlocutor or a generated        output from the computer interlocutor.    -   e. receiving a plurality of transcribed conversations comprising        a plurality of topics comprising a plurality of inputs and        outputs by the interlocutors;    -   f. accessing and using for rule-based decision making a        plurality of metadata related to a plurality of conversations,        topics, interlocutors, or related computer systems;    -   g. receiving conversation data from transcribed conversations        between one or more of people, an online chat or a text        messaging system, a speech recognition system, and a chatbot or        voicebot system (in some embodiments, some users' paths may be        given more weight than other users);    -   h. splitting a plurality of transcribed conversations into a        plurality of groups related to a conversation ontology using a        plurality of metadata;    -   i. identifying a plurality of dominant paths comprising a        plurality of conversational behavior by counting the frequency        of occurrences of said behavior for a given path;    -   j. creating a conversation model comprising plurality of        conversation behaviors, metadata, and dominant paths; and    -   k. accessing and using for rule-based decision making the        conversation model to assign a probability score for a matched        input to the computer interlocutor or a generated output from        the computer interlocutor.

Referring now to FIG. 1 , an exemplary improved networked computerenvironment 100 is depicted according to the present invention. Theconversation classifier server 101B is connected to a network 103 andconfigured such that is it capable of storing and running one or more ofthe following: a conversation processor 104, a conversation classifier105, a topic classifier 106, a dominant path modeler 107, and aconversation modeler 108, each of which may be realized by a processorrunning computer instructions, specialized electronic hardware circuits,or a combination of both. In this exemplary embodiment, another computer101A is also connected to the computer communications network 103 andcontains conversation data 102, which consists of transcribedconversations between two or more human and/or computer interlocutors.In some embodiments, at least one of the interlocutors may be interfacedvia an application programming interface (API). In some embodiments, allof the interlocutors may be conducting a dialog within one computer.

Referring now to FIG. 2 , exemplary methods used by the data processingsystem 100 to classify a plurality of conversation transcriptions fromconversation data 102 between two or more interlocutors 200 are setforth further reference the exemplary arrangement of computing systemsas shown in FIG. 1 . The first step of the process is to segment theconversation transcript into turns further categorized by interlocutor201 which is performed, for example, by the conversation processor 104and further illustrated in FIG. 3 . The conversation is furtherclassified 202 according to a conversation ontology 700 according toconversation class 304. In at least one embodiment, the segmenting of aconversation transcript may be performed manually, according to theconversation ontology described herein, or may be performed at least ifnot entirely automatically using available third-party dialog actprocessing systems with suitable control parameters.

Next, conversations are weighted 203 according to the number of pathtraversals, which is performed, for example, by the dominant pathmodeler 107. Following the previous step, the data processing systemperforms topic classification 204 using the topic classifier 106. Topicclassification can be performed automatically (unsupervised) usingtechniques such as keyword analysis thesauri, and natural languageprocessing. Finally, the improved data processing system creates 205 aweighted conversation model 600 as further illustrated by FIG. 6 whichcan be used by a plurality of computer interlocutor systems to improveinput and output performance in a number of ways, including but notlimited to:

-   -   (a) allowing for predictive responses by automated systems in        order to handle transactions faster, thereby reducing the        computer resources consumed by aggregate transactions and        allowing more transactions to by handled by the same amount of        hardware;    -   (b) supporting optimized product design and upgrades by        identifying and automating the most likely conversation        behaviors to target in resource reduction (decrease response        time, reduce memory footprint, reduce processor burden, reduce        communications bandwidth, etc.); and    -   (c) increasing customer affinity for interacting with automated        systems by reducing delays between conversation turns which are        otherwise unnatural delays when two humans are conversing.

FIG. 3 illustrates an exemplary embodiment 300 of a method for adominant path weighting 203 and output of the conversation classifier105. This example includes a series of conversation turns T₁-T₁₂ 301 byan interlocutor 302 and another interlocutor 303 and further classifiedinto a conversation classes 304 which correspond to a conversationontology 700 as further illustrated in FIG. 7 .

The conversation classifier 105 works by examining the text from theinterlocutor 305 comprising a turn 301 and further examines the secondinterlocutor's text 306, which, together and with processing ofsubsequent text including the turns of the interlocutors, classifies theturns into a conversation class 304. Illustrative of this figure, theconversation classes are greeting 307, topic negotiation 308, discussion309, change/end topic 310, and end conversation 311.

FIG. 4 shows, using a Sankey-like diagram, an exemplary 400 dominantweighting method 203 used, for example, by the dominant path modeler 107of data processing system 100 based on a plurality of segmentedtranscribed conversations processed by, for example, the conversationclassifier 105 as depicted in FIG. 3 . FIG. 4 further illustrates ahighlighted dominant path example as produced by the dominant weightingmethod 203 comprised of a plurality of classified conversations 300. Thedominant path model 400 is created, for example, by the dominant pathmodeler 107. Each step in the dominant path may be representative of aconversation class (304), an interlocutor input, or additional metadataidentified by the dominant path modeler. FIG. 4 illustrates a dominantpath model and may include a greeting 401, a topic negotiation 403, atopic discussion 405, a change or end of topic 407, and an end ofconversation 409 steps (path nodes). The illustrated lines between eachelement of the dominant path represent the sum of plurality ofconversations that traverse each path. The lines or weights (402, 404,406, and 408) between steps in the paths represent the sums W₁-W _(N) oftraversals between steps in the dominant path.

FIG. 5 depicts 500 an exemplary topic classification method 204 used,for example, by the topic classifier 106 of data processing system 100,and is used to identify the correct topic of conversation based on aplurality of segmented conversations 300 including a plurality of topicnegotiation segments 308. FIG. 5 further includes matching interlocutorinputs 501 to a plurality of topics in a plurality of domain ontologies502 which returns the resulting metadata associated with a plurality ofmatching topics 503 to, for example, the topic classifier 106.

FIG. 6 depicts an exemplary weighted conversation model 600 which isrecorded in computer memory in an improved data structure and produced,for example, by the conversation modeler 108 of the data processingsystem 100, using, for example, the weighted conversation modelingmethod 205 from a plurality of transcribed conversations for a pluralityof identified topics 500. FIG. 6 is illustrative of the weightedconversation modeling method 205 which is produced by the conversationmodeler 108 and includes a topic 601 and a plurality of weights 602,603, 605, 607 associated with a plurality of conversation paths andturns 604, 606, 608. The present method uses the output of, for example,the dominant path modeler 107 and its associated dominant path weightingmethod 203 and as previously illustrated in FIG. 4 as input.

Each path segment P₁-P_(N) between turns T₁-T_(N) from a given dominantpath model 400 and its associated weights W₁-W_(N) are converted to acorresponding weight in the conversation model 600 such that thepercentage of conversation traversals are represented as a percentage ofthe total traversals from the plurality of processed conversations.

For this present illustration, given a topic 601, weight 602 representsthe percentage of processed conversations that have traversed the pathP_(x) for the interlocutor turn T_(y). Further, weight 603 represents asecond dominant path weighting with its associated path and interlocutorturn. Further weights for turns by the interlocutors are similarlyrepresented by 605, 606, 607, and 608 as prescribed by the conversationsegments, paths and weights contained in the dominant path model 400.The resulting conversation model as illustrated by FIG. 6 and itsassociated weights can then be used as by a method to predict the nextmost likely step in a conversation based upon the current position inthe conversation model.

Referring now to FIG. 7 , an exemplary conversation ontology is shownusing a steampipe-like diagram, which may consist of entities includinga greeting 701, topic negotiation 702, a discussion about a topiccomprised of a series of turns 709 between the interlocutors that maycontain a corresponding question 703 and answer followed by an end 705or change of topic 708 followed by an end of conversation 706.Conversation repair 707 occurs within a topic when one or bothinterlocutors exchange turns during which the initial or earlier topicis finetuned or further refined, but not entirely changed from onedomain to another. A plurality of conversation ontologies may be used bythe data processing system 100 and one or more of the correspondingmethods 200 of the system. Further, an ontology 700 is specificallyutilized by the conversation classifier 105 and the associated methodconversation classification 203 and as further illustrated by FIG. 3 tosegment a plurality of conversations into conversation classes 304.

Referring now to FIG. 8 , an exemplary arrangement 800 of computers,devices, and networks according to at least one embodiment of thepresent invention is shown. A variety, but not exhaustive collection, ofinterlocutor types are shown, including a computer 804 a, such as apersonal computer or tablet computer, a smart cellular telephone 804 b,a traditional telephone 804 c, a chat server 805 a, a web server 805 b,an interactive voice response (IVR) system 805 c, and an agent console805 d, which are interconnected via one or more wired or wirelesstelephone networks 801, data networks 803, and an internet 801. Two moreor more of the interlocutor devices can carry on a dialog orconversation, which can be processed according to the forgoingdescriptions. This analysis, as described, yields conversation data withmetadata 102, which is created via supervised conversation analysis 807,automated conversation analysis 806, or a combination of both. Theconversation classifier server 101 b then communicates via appropriatedata networks to access the conversation data 102 and perform theforgoing dominant path analysis.

The preceding example logical processes may include computer processinghardware to embody systems according to the present invention; may becoupled with tangible, computer readable memory devices to realizecomputer program products according to the invention; and may beembodied as a machine logic method.

The present invention may be realized for many different processors usedin many different computing platforms, including but not limited to“Personal Computers” and web servers, running a popular operatingsystems such as Microsoft™ Windows™ or IBM™ AIX™, UNIX, LINUX, GoogleAndroid™, Apple iOS™, and others, to execute one or more applicationprograms to accomplish the computerized methods described herein,thereby providing the improvement to the computer platform as set forthherein.

The “hardware” portion of a computing platform typically includes one ormore processors accompanied by, sometimes, specialized co-processors oraccelerators, such as graphics accelerators, and by suitable computerreadable memory devices (RAM, ROM, disk drives, removable memory cards,etc.). Depending on the computing platform, one or more networkinterfaces may be provided, as well as specialty interfaces for specificapplications. If the computing platform is intended to interact withhuman users, it is provided with one or more user interface devices,such as display(s), keyboards, pointing devices, speakers, etc. And,each computing platform requires one or more power supplies (battery, ACmains, solar, etc.).

The terminology used herein is for the purpose of describing particularexemplary embodiments only and is not intended to be limiting of theinvention. As used herein, the singular forms “a”, “an” and “the” areintended to include the plural forms as well, unless the context clearlyindicates otherwise. It will be further understood that the terms“comprises” and/or “comprising,” when used in this specification,specify the presence of stated features, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, steps, operations, elements, components, and/orgroups thereof, unless specifically stated otherwise.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below are intended toinclude any structure, material, or act for performing the function incombination with other claimed elements as specifically claimed. Thedescription of the present invention has been presented for purposes ofillustration and description, but is not intended to be exhaustive orlimited to the invention in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the artwithout departing from the scope and spirit of the invention. Theembodiment was chosen and described in order to best explain theprinciples of the invention and the practical application, and to enableothers of ordinary skill in the art to understand the invention forvarious embodiments with various modifications as are suited to theparticular use contemplated.

Certain embodiments utilizing a microprocessor executing a logicalprocess may also be realized through customized electronic circuitryperforming the same logical process(es). The foregoing exampleembodiments do not define the extent or scope of the present invention,but instead are provided as illustrations of how to make and use atleast one embodiment of the invention.

What is claimed is:
 1. A computer-based method to create one or moredigital models of interlocutory conversations comprising: splitting, bya computer processor, conversation text data into groups related to atleast one conversation ontology using metadata associated with theconversation text data; identifying, by a computer processor, one ormore dominant paths of conversational behavior between the groupsaccording to the metadata, wherein each of the one or more dominantpaths comprises a plurality of path segment traversals betweenconversation turns in the conversation text data; and creating, by acomputer processor, a digital conversation model in non-transitorycomputer-readable memory device containing the identified one or moredominant paths of conversation behavior, wherein non-transitorycomputer-readable memory device is not a propagating signal per se. 2.The method of claim 1 wherein the at least one conversation ontologydefines the groups comprising at least a greeting group, a topicnegotiation group, a topic discussion group, a change/end of topicgroup, and an end-of-conversation group.
 3. The method of claim 2wherein the groups further comprise a topic repair group.
 4. The methodof claim 1 wherein each group comprises one or more conversationalturns, wherein each conversational turn is associated with aninterlocutor device.
 5. The method of claim 1 wherein the creatingcomprises creating digital conversation model further comprises:creating, by a computer processor, a data structure stored innon-transitory computer-readable memory device which is not apropagating signal per se; creating, by a computer processor, in thedata structure, at least one top-level topic record, wherein the atleast one top level topic record comprises a plurality of weight valuesfor conversational paths arriving to a topic from at least two previousgroups; wherein the plurality of weight values represent historicalconversational behaviors leading to the topic and are predictive offuture conversational behaviors about a same topic.
 6. The method ofclaim 1 wherein the creating a digital conversation model furthercomprises: creating, by a computer processor, a data structure stored ina non-transitory computer-readable memory device which is not apropagating signal per se; creating, by a computer processor, in thedata structure, at least one top-level topic record, wherein the atleast one top-level topic record comprises a plurality of weight valuesfor conversational paths departing from a topic to at least two nextgroups; wherein the plurality of weight values represent historicalconversational behaviors leading away from the topic and are predictiveof future conversational behaviors about a same topic.
 7. The method asset forth in claim 1 wherein the metadata comprises marks associatedwith conversational turns which indicate a conversational group to whicheach conversational turn belongs.
 8. The method as set forth in claim 1wherein the metadata comprises one or more path segment traversalcounts, path segment traversal frequencies, path segment traversalstatistics, or a combination of path segment traversal counts, pathsegment traversal frequencies and path segment traversal statistics,associated with each group, each topic, and each conversational pathbetween groups in the conversational text data.
 9. The method of claim 8wherein one or more dominant paths of conversational behavior areindicated by the one or more path segment traversal counts, frequenciesor statistics which exceed a threshold, wherein the one or more dominantpaths are paths of conversation which are most expected to lead to ordepart from a particular group.
 10. The method of claim 1 wherein theconversation text data comprises transcriptions from one or more sourcesconsisting of an online chat or a text messaging system, a speechrecognition system, a chatbot and a voicebot system.
 11. The method ofclaim 1 wherein the splitting comprises, at least in part: providing, bya computer processor, the conversation text data to a human interfacedevice; and receiving, by a computer processor, the groups from thehuman interface device.
 12. The method as set forth in claim 1 whereinthe conversation text data comprises a plurality of conversations,wherein at least one common interlocutor is included in all theplurality of conversations.
 13. The method as set forth in claim 1wherein the conversation text data comprises a plurality ofconversations, wherein more than two different interlocutors areincluded within the plurality of conversations.
 14. The method as setforth in claim 1 wherein the conversation text data comprises turns fromat least one automated interlocutor.
 15. A non-transitory computerprogram product to create one or more digital models of interlocutoryconversations comprising: a tangible, non-transitory computer-readablememory device which is not a propagating signal per se; and programinstructions encoded by the tangible, non-transitory computer-readablememory device which, when executed by a processor, perform: splittingconversation text data into groups related to at least one conversationontology using metadata associated with the conversation text data;identifying one or more dominant paths of conversational behaviorbetween the groups according to the metadata, wherein each of the one ormore dominant paths comprises a plurality of path segment traversalsbetween conversation turns in the conversation text data; and creating adigital conversation model in a a non-transitory computer-readablememory device containing the identified one or more dominant paths ofconversation behavior, wherein the a non-transitory computer-readablememory device is not a propagating signal per se.
 16. The non-transitorycomputer program product of claim 15 wherein the at least oneconversation ontology defines the groups comprising at least a greetinggroup, a topic negotiation group, a topic discussion group, a change/endof topic group, and an end-of-conversation group, and wherein each groupcomprises one or more conversational turns, wherein each turn isassociated with an interlocutor device.
 17. A system to create one ormore digital models of interlocutory conversations comprising: acomputer processor; a tangible, non-transitory computer-readable memorydevice which is not a propagating signal per se; and programinstructions encoded by the tangible, non-transitory computer-readablememory device which, when executed by the computer processor, perform:splitting conversation text data into groups related to at least oneconversation ontology using metadata associated with the conversationtext data; identifying one or more dominant paths of conversationalbehavior between the groups according to the metadata, wherein each ofthe one or more dominant paths comprises a plurality of path segmenttraversals between conversation turns in the conversation text data; andcreating a digital conversation model in a a non-transitorycomputer-readable memory device containing the identified one or moredominant paths of conversation behavior, wherein the a non-transitorycomputer-readable memory device is not a propagating signal per se. 18.The system of claim 17 wherein the at least one conversation ontologydefines the groups comprising at least a greeting group, a topicnegotiation group, a topic discussion group, a change/end of topicgroup, and an end-of-conversation group, wherein each group comprisesone or more conversational turns, wherein each turn is associated withan interlocutor device.